External search strategy and innovation performance. Effects on employee loyalty in the German automotive industry

Master's Thesis, 2015
51 Pages, Grade: 1,3



External knowledge sources are nowadays commonly accepted to be an important element for firm’s innovative performance. Therefore, this research helps to get new insights into the level of firm’s openness behavior referring to its search strategy and innovation performance. Beyond, I try to find evidence for employee loyalty between high performing and other firms. In detail, the subject here is to explore open innovation by analyzing what dimensions of firm’s external search channels are affecting innovation outcomes and whether higher innovation performance could be related to employee loyalty and satisfaction in the R&D department of the German automotive industry.

In this study three dimensions of external search strategies that affect firm’s innovation performance are investigated. The two concepts of external search breadth and depth that both look into the subject of “how” firm’s access external knowledge are introduced. The third concept shows the relevance of choosing the right partners for the innovation process out of a wide range of external sources emphasizing “with whom to interact with" (Arruda et al., 2013). Quantitative research within a mail survey was used for data collection purposes. Based on the survey, I found that searching intense and deeply present a curvilinear relation (taking an inverted U-curve) between the search strategy and firm’s innovation performance, and discovered the presence of a point of “over-search”. Regarding widely searching this study indicates that the diversity of different external partners possesses a positive effect on innovation performance of German automotive firms. Next, I found that the level of openness within the innovation process provides evidence that firm’s innovation performance depends on different external actors. In particular, customers, other companies in the holding and suppliers possess a significant impact. Finally, the results indicate that open innovation positively affects innovation performance among German automotive firms and that high-innovators tend to have more satisfied and consequently loyal R&D employees than low performing firms.

Table of Contents

1. Introduction ... 1

1.1. Research Gap and Research Question ... 1

1.2. Research Object ... 2

1.3. Work Structure ... 4

2. Literature Review ... 5

2.1. Open Innovation ... 5

2.2. External Search Strategy ... 6

2.3. Innovation Performance ... 7

2.4. Power of Loyalty and Satisfaction ... 8

3. Theoretical Frame of Reference ... 10

3.1. Innovation Process and External Knowledge Sources ... 10

3.2. External Search Breadth ... 11

3.3. External Search Depth ... 12

3.4. External Innovation Partners ... 13

3.5. Employee Loyalty in Matters of Innovation Performance ... 15

4. Empirical Study ... 17

4.1. Data and Methodology ... 17

4.2. Measures ... 18

4.2.1. Types of External Innovation Partners ... 18

4.2.2. Measures for External Search Breadth ... 18

4.2.3. Measures for External Search Depth ... 18

4.2.4. Measures for Innovation Performance ... 19

4.2.5. Measures for Employee Loyalty ... 20

5. Results ... 21

5.1. Descriptive Statistics ... 21

5.2. Results of the Regression Analysis for Innovation Performance ... 22

5.3. Employee Loyalty and Innovation Performance Analysis ... 26

6. Conclusion ... 29

References ... 33

Appendix ... 41

1. Introduction

1.1. Research Gap and Research Question

A constantly changing environment through increasing globalization, shorter time-to-market periods, intense competition, and the uprising need to win the race for talents are trends that companies can only manage if they innovate (Chen et al., 2011). Companies start to realize that fully relying on its internal Research and Development (R&D) capabilities is risky and expensive in an increasingly open business world. Therefore, co-operations and interactions with external channels are shifting more and more into focus. The open innovation model was framed to manage these uprising challenges by interacting with external innovation sources (Chesbrough, 2003a). In the literature open innovation is also commonly assimilated with “technology acquisition” and “technology exploitation” (Lichtenthaler, 2008). However, technological innovation has often an uncertain outcome since only a fraction of innovations lead to new products and services that make it to the market successfully (Chen et al., 2011). Therefore, particularly technology-based industries have to explore new ways of innovation to escape this productivity dilemma (Ili et al., 2010).

The German automotive industry is the largest industry sector in Germany and it is strongly relying on cutting-edge technologies. Nowadays, it is the most innovation-intensive German industry with a percentage of sales out of new products and services of over 50%. The budget in 2014 is estimated on 47,1bn € and innovation expenses amount about 10,2% of sales.[1] However, for decades the automotive industry was not considered to suit into the open innovation model because of its historically large investments in internal R&D (Ili et al., 2010). Through the need for increasing innovation and cost pressure in the industry, the Original Equipment Manufacturers (OEMs) explored new ways to achieve firm success and profitability. One important method was the tendency to turn away from solely internal R&D towards external knowledge sourcing (Ili et al., 2010). Even though several studies about the positive impact of external search channels on firm performance (Salomo et al., 2008; Tsai and Wang, 2008) exist, it is still not clear, what the significant dimensions are in achieving superior innovation performance in the German automotive context. Therefore, this thesis explores external search breadth and depth as models of external search strategy, and identifies the relevant external innovation partners to maximize innovation performance.

When it comes to innovation and performance the focus of attention should be also shifted towards R&D employees. In a competitive and globalized industry, as the German automotive one, become employees the foundation for firm performance and productivity (Homburg et al., 2009). Laborers are directly involved in innovation activities and therefore responsible for the innovation outcome. It is also considered that satisfied employees act more loyal to the company and have a significant impact on firm profits (Yee et al., 2010). However, employee-firm relations were barely tested in the context of innovation performance. Therefore, I want to explore whether firm’s innovation performance could have a positive impact on employees in building a loyal relationship towards the company.

1.2. Research Object

Open innovation has been introduced as a new paradigm of innovation management (Chesbrough, 2003a). The approach refers to the systematic opening of company boarders for inside-out and outside-in movements of technologies and ideas. Hereby, it enhances internal innovation and expands markets for external innovation acquisition (Chesbrough, 2003a; Lichtenthaler, 2008). One main characteristic is the integration of external partners throughout the innovation process (Cheng and Huizingh, 2014). This implies that companies have to look for external sources beyond its business segment (Malik and Wei, 2011; Huizingh, 2011).

Child et al. (2005) identify various motivations for companies to cooperate with external partners during its innovation activity such as better market access and joint product development. Both might lead to an increase in firm’s competitiveness, sales and profitability. However, implementing open innovation activities may also hold barriers that can decrease the effectiveness of the innovation process such as the asymmetry in learning and power relations, and cultural aspects (Hladik, 1988; Hamel, 1991). After underlining possible opportunities and risks for co-operations during the innovation process, I want to take a deeper look at the searching strategy. To investigate the influence of the external search strategy on innovation performance I base this work on the framework of Laursen and Salter (2006) and Katila and Ahuja (2002) who advance the view that the involvement of external partners achieves and sustains innovation. Furthermore, I complement their findings by exploring whether employee loyalty can be explained by higher firm’s innovation performance.

Laursen and Salter (2006) developed their concept of external search breadth and depth based on the investigation of Katila and Ahuja (2002) who highlight the importance of intense co-operation and interaction with external partners (external search depth) as well as the involvement of a wide range of external sources (external search breadth) throughout the innovation process. In addition, Belderbos et al. (2004) and Miotti and Sachwald (2003) show the relevance of choosing the right external innovation partners to co-operate with. The impact of external search strategies on innovation performance of German automotive firms will be analyzed by gathering empirical evidence in form of a questionnaire. The loyalty variable was composed of employee perceptions and beliefs to show a possible discrepancy between highly innovative companies and others. The elementary data about the German automotive sector is provided by CIS, the Community Innovation Surveys[2]. Figure 1 provides the conceptual framework of this thesis.

Figure 1: Conceptual Framework

[Figures and tables are omitted from this preview.]

1.3. Work Structure

The thesis is structured in five chapters of theoretical review and analysis. The second chapter covers the literature review including the definition of open innovation having the focus on external search strategy, innovation performance, and the importance of loyalty. The third chapter explains the theoretical frame of reference. Here, the topics of a general innovation process and external knowledge sources are covered distinguishing external search breadth and depth as well as external innovation partners. Later, the study looks at the relationship between employee loyalty and innovation performance. Chapter four includes the empirical study by describing the methodology and measurements of the key points affecting innovation performance in the German automotive industry, as well as the measure for employee loyalty. The fifth chapter presents results and models used to analyze the research question and hypotheses. Finally, a discussion of the main results is presented in the conclusion.

2. Literature Review

2.1. Open Innovation

Henry Chesbrough introduced the idea of open innovation in his book Open Innovation: The New Imperative for Creating and Profiting from Technology (2003a) as the new paradigm in innovation management and described it later on as “… the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively“ (Chesbrough et al., 2006, p.2).

Although it is a new coined term, open innovation was formed out of various ongoing developments from the past. Some included concepts were the Not Invented Here (NIH) syndrome of Katz and Allen (1982), the lead user approach of von Hippel (1986), the consideration of complementary assets of Teece (1986), and the absorptive capacity concept of Cohen and Levinthal (1990). Beyond, also present challenges had formed the open innovation term. Changes in the business environment characterized by changing customer requirements, new technologies and the mobility of skilled labor made it difficult for companies to keep its strategic advantage from internal R&D (Chesbrough, 2003a; 2006). This means that companies need to acquire external knowledge, ideas and technologies to accelerate its internal innovation process. In addition, unused internal knowledge should be monetized through external paths to market (Chesbrough, 2003a; 2003b).

Therefore, the open innovation model implies several internal and external search channels (West and Gallagher, 2006). Firms can open up its innovation process in two directions, either inbound or outbound (Lichtenthaler and Ernst, 2009; Lichtenthaler, 2009). Inbound open innovation activities correspond to firms’ ability of acquiring and exploiting external knowledge from co-operating partners and sources such as suppliers, customers, competitors, research institutes, universities and governments (Faems et al., 2005; Tether and Tajar, 2008; Cheng and Huizingh, 2014). Furthermore, it describes the ability of leveraging external discoveries since firms do not need to rely solely on their own R&D capabilities anymore (Chesbrough and Crowther, 2006). Previous research in this area has covered networking with external actors (Dittrich and Duysters, 2007; Enkel, 2010) and the integration of new innovative ideas (Piller and Fredberg, 2009).

Outbound open innovation activities imply external exploitation of firms’ internal knowledge. It includes patents, licensing out, outsourcing of internal knowledge, and firm spin-offs (Lichtenthaler and Ernst, 2009; Cheng and Huizingh, 2014). Studies on outbound innovation activities include co-operation, partnerships, licensing and alliances (Lichtenthaler and Frishammer, 2011), and commercialization of unused internal knowledge and technologies in new and uprising markets (Enkel and Gassmann, 2010; Chesbrough and Crowther, 2006). As open innovation encompasses a wide part of activities (Cheng and Huizingh, 2014) with different openness levels (Huizingh, 2011), this work is focusing mainly on the inbound concept by acquiring external knowledge from different sources to explore its effect on firm’s innovation performance.

2.2. External Search Strategy

According to Chesbrough (2003a) plenty of innovative firms shifted to a more open business approach using external partners to innovate. An important part of opening the innovation process is the search strategy used to acquire new technologies and ideas. Research shows that search strategies have a significant impact on innovation performance (Katila, 2002; Katila and Ahuja, 2002). Cohen and Levinthal (1990) highlight that the ability to exploit knowledge from external sources is a crucial part of innovation performance. Companies were also adopting open search strategies that imply deeper or wider search to achieve a more sustainable innovation (Laursen and Salter, 2006). Over the years uprising models of innovation identified that successful innovators heavily rely on their interaction with external players throughout the innovation process, including lead users, suppliers, and many different institutions (von Hippel, 1988; Ludvall, 1992; Szulanski, 1996). Chesbrough (2003a, 2003b) suggests that innovative firms do not need to spend high investments in internal R&D but rather have to develop the ability to successfully innovate through exploitation of knowledge available from a wide range of external sources through the utilization of an effective external search strategy.

2.3. Innovation Performance

Innovation performance is a very fragmented research area. The term originally refers to how successful firms were in introducing new products or services to the market (Henard and Szymanski, 2001; Montoya-Weiss and Calantone, 1994). Many research studies about measuring innovation performance were conducted, using new product or service innovativeness (Atuahene-Gima and Wei, 2011), the degree of success of new products and services (Blazevic and Lievens, 2004; Baker and Sinkula, 2007), or even the percentage of sales (Im and Workman, 2004). Other research studies imply various different performance measures to analyze innovation performance from different perspectives (Henard and Szymanski, 2001; Im and Workman, 2004). Out of this overall collection the following four dimensions are selected to demonstrate innovation performance (Cheng and Huizingh, 2014; Arruda et al., 2013):

- New product or service innovativeness

- New product or service success

- Better customer performance

- Better firm’s financial performance

The first dimension is new product or service innovativeness. It refers to the novelty level of the innovation introduced to the market (Garcia and Calantone, 2002; Salomo et al., 2008). The second one, success of new products or services in the market, is measured by firms’ ability to compete (Baker and Sinkula, 1999). The third dimension is customer performance that covers the large area of customer satisfaction and loyalty (Blazevic and Lievens, 2004). Finally, the last dimension is financial performance of firms in the market. This can be analyzed through financial success of new products and services referred to firm’s profitability (Im and Workman, 2004). The measurement of innovation performance must not only be based on financial measures but also on different cycle time measures (Griffin, 1993; Knudsen and Mortensen, 2011). This method allows a comparison of unequal innovation activities, even if the firms are not active in the same sector. So, additional three dimensions will be added to complete the measures for innovation performance (Arruda et al., 2013):

- Greater speed of the innovation process

- Superior quality of the product or service

- Less costs of the innovation process

2.4. Power of Loyalty and Satisfaction

The power of loyalty is an essential element in the business world with significant impact on company’s performance (Harter et al., 2002). Moreover, it became an indicator to identify innovative companies that possess the ability to serve uprising customer needs in the markets (Reichheld and Teal, 1996). In this sense, achieving loyalty must be a central objective in every company.

Plenty of articles, forums, conventions and best practice examples exist on how to enhance loyalty efforts (Reichheld, 2003). According to Foster et al. (2008) and Reichheld (2001) companies with more loyal customers, employees or shareholders generate even higher sales and profits. Hence, from a company’s perspective, it is very important to increase the number of loyal customers or at least maintain them to stay competitive in the market. However, companies cannot earn customer loyalty before earning employee loyalty and satisfaction first (Reichheld, 2003; Reichheld 2006).

In the literature, employee satisfaction is often linked to customer satisfaction, and therefore causally related to firm and industry performance (Schneider et al., 2003; Homburg et al., 2009). Employee satisfaction can be described as the result of a person’s positive perceptions to his or her work or company (Homburg and Stock, 2004). Further research studies show a positive and significant relation of employee satisfaction with employee loyalty towards their company, as well as a negative relation with their purpose to leave (Griffeth et al., 2000; Hom and Kinicki, 2001; Martensen and Gronholdt, 2001). Michlitsch (2000) concludes in his research on employee loyalty that high-performing employees are the key for a successful business model. Yee et al. (2010) explore a significant impact of loyal employees on firm’s financial performance. Accordingly, satisfied and therefore loyal employees are the key of any successful performing company. Several studies already analyze loyalty among different performance dimensions (Yee et al., 2010; Reichheld and Teal, 1996; Michlitsch, 2000; Homburg et al., 2009), however, the influence of innovation performance on employee loyalty and satisfaction was barely tested. Therefore, this research is exploring whether higher firm’s innovation performance could be decisive for greater employee loyalty towards the firm.

3. Theoretical Frame of Reference

3.1. Innovation Process and External Knowledge Sources

Innovation can be seen as a process with sequential and interconnected activities (Van de Ven et al., 1999; Svetina and Prodan, 2008). The first Schumpeterian innovation model proposed a non-integrated model of innovation where firms develop and commercialize new technologies on their own (Schumpeter, 1942). In this model innovation was treated as a linear progression of processes performed by firms internally. The complete development of an innovation from basic research to product launch in the market was following a formal and inflexible internal process (Godin, 2006; Svetina and Prodan, 2008).

More recently this non-linear and more “closed” innovation model has changed. Many firms now cooperate across industries with external sources (Cohen and Levinthal, 1990; Tsai and Wang, 2008). Chesbrough (2003a, 2003b) emphasizes the benefit of using external knowledge sources through increasing inter-firm technology transfer to manage a successful innovation process. He states that this concept enables companies to discover new ideas, lower its innovation risk and increase the speed to market. Accordingly, firm’s searching behavior and external search strategy assume great importance when analyzing innovation performance (Katila and Ahuja, 2002). An external search strategy may be defined as a company’s decision to select the best possible way to acquire and exploit external knowledge (Laursen and Salter, 2006). A company’s external search strategy is widely shaped through external environmental factors such as the availability of technology, the framework of the innovation system and the degree of complexity and turbulence (Cohen and Levinthal, 1990; Klevorick et al., 1995). On the other hand, Laursen and Salter (2006) propose another important characteristic. They claim that managers past experiences and future expectations influence companies’ external search strategy as well. Nevertheless, the most crucial part of an external search strategy is its level of effectiveness, which is affected by how the company manages its search processes of exploiting new ideas, external knowledge and technologies (Arruda et al., 2013). Although, organizations possess these capabilities it is still difficult for them to determine a leading search strategy (Levinthal and March, 1993). Empirical research shows that to develop the best strategy it is important to focus on the industry the company operates in, the type of knowledge explored and the novelty of innovation (Criscuolo et al., 2011; Garcia and Calantone, 2002). These are reasons why it is worthwhile investigating the development of an effective external search strategy. Therefore, it becomes more important to dig deeper into the dimensions that affect company’s search strategy and its effectiveness.

The authors Katila and Ahuja (2002) suggest to concentrate on two dimensions throughout the development process of an external search strategy. The first dimension implies deep and intense interaction with external sources. The second one consists of the broad diversity of interaction with external actors. In the German automotive context broader and deeper search might enable companies to develop greater abilities to adapt changes and hence to innovate successfully. A third dimension is proposed by Miotti and Sachwald (2003) and Belderbos et al. (2004) that contains the free selection of which external actor to interact with.

3.2. External Search Breadth

Laursen and Salter (2006) developed the concept of Katila and Ahuja (2002) in order to analyze the consequence of broader and deeper searching on innovation performance. The first concept is external search breadth that is related to the variety of external actors the company interacts with, which the authors define as “the number of external sources and search channels that firms rely upon in their innovative activities.” (Laursen and Salter, 2006, p.134). Katila and Ahuja (2002) stress that companies that explore and acquire new knowledge and solutions use mainly breadth as their external search strategy. Prior research has demonstrated that the more external partners the company interacts with the higher the innovation performance becomes (Katila and Ahuja, 2002). In the German automotive context the interaction with various external partners became an indispensable part in developing new technologies and products, accelerating time-to-market and meeting market expectations (Ili et al., 2010).

However, Katila and Ahuja (2002) also argue that external search breadth can cause a negative impact on the search process. The authors state that the scanning process of a wide range of sources and the heavy involvement of external actors becomes the more intense it gets less beneficial, rather than the contrary. They argue that a cost of integration of knowledge exists which may overrun the benefits of new knowledge discoveries. Taking positive and negative effects into account, empirical studies (Laursen and Salter, 2006; Leiponen and Helfat, 2010) come to the conclusion that the number of different external actors the company interacts with is curvilinearly (taking an inverted U-curve) related to innovation performance. Based on this, the first hypothesis has been framed as followed:

Hypothesis 1. External search breadth has a positive effect on innovation performance of German automotive firms.

3.3. External Search Depth

As seen above, the variety of external actors presents the importance of a broader search process. However, regarding the external search strategy not only a wide range of external actors is relevant but also the intensity of interacting with them. In this sense, the second concept refers to external search depth. Here, the relevant perspective lies in the analysis of deep interactions with external sources and defines how deeply to draw from different external search channels (Laursen and Salter, 2006). This is the dimension where the company exploits valuable knowledge of external actors it interacts with, according to Katila and Ahuja (2002). Levinthal and March (1981) conclude that through iterative processes and the usage of same knowledge elements the possibility of emerging errors is reduced significantly, and routines are developed and strengthened. All these activities add reliability to the innovation process and trust in mutual co-operation. In this sense, the iterative application of knowledge acquired from external partners may help the company to assimilate and even expand its innovation competencies (Katila and Ahuja, 2002). Therefore, the integration of external technology and strong co-operation with other industry actors are crucial for increasing innovation power, and indicate the importance of increasing technology intensity (Ili et al., 2010).

However, Dosi (1988) claims that sooner or later there is a limitation to the process of drawing deeply from external sources. As regards that, Katila and Ahuja (2002) discover two criteria. At first the authors point out the existence of a cost where after a certain point the innovation activity of the same source becomes more costly, while at the same time the solution becomes far too complex. They named it the point of “over-search”. The second criterion is related to the fact that the deeper the interaction with a single external partner goes the less flexible and more rigid the innovation process becomes (Argyris and Schön, 1978).

Hence, Katila and Ahuja (2002) and Laursen and Salter (2006) suggest that in principle greater reliance on a single external knowledge sources has a positive outcome until it reaches the point of “over-searching” where deeper digging becomes disadvantageous in terms of firm’s innovation performance. Therefore, the second hypothesis can be stated followed:

Hypothesis 2. External search depth is curvilinearly (taking an inverted U-curve) related to innovation performance of German automotive firms.

3.4. External Innovation Partners

The degree of interaction and the diversity of external sources are essential dimensions of the external search strategy. The German automotive industry is a fast-paced and highly competitive environment that heavily invests in R&D and new product development (NPD). The industry players depend on the most and best idea creation. Therefore, interaction with external actors is shifting into their focus of innovation developments (Ili et al., 2010). The OEMs are surrounded by external knowledge sources with whom they could interact. Under many possible external innovation partners, I decided to use the selection of Arruda et al. (2013) which was also conducted and analyzed by ZEW, the Centre of European Economic Research, relative to the period 2012, and published in Community Innovation Surveys (CIS)[3] most recent in 2013. The selected actors are:

- Customers

- Competitors

- Other Companies in the Holding

- Suppliers

- Universities and Research Institutes

These five external actors are likely to lead to positive innovation outcomes for German automotive firms, according to CIS. Customers compose in general an external information source of knowledge about technologies, market characteristics and customer needs (Arruda et al., 2013). In the innovation phase an involvement or collaboration with customers tends to accelerate this process. Furthermore, it may reduces market risks associated with new product or service introductions (von Hippel, 1988), making customers an important co-developer or inventor when offering new ideas, concepts for prototype developments, or complex products during the initial project phase of innovations (Tether, 2002; Lettl et al., 2006). Moreover, von Hippel (1986) shows the importance of interacting with customers whose needs represent the market and form future market needs, which he defined as lead users. Further actors that can reduce costs, improve product or service quality, and, as a consequence, speed up the innovation process are competitors and suppliers (Clark, 1989; Dyer, 1996; Ragatz et al., 1997). Supplier involvement in the early stages of the innovation process, for example in the conceptualization or design stage, can be beneficial according to Bidault et al. (1998). On the other hand, it poses the risk that sensitive knowledge could leak out of the company (Mikkola and Larson, 2006).


[1] ZEW Innovationen Branchenreport: Ergebnisse der deutschen Innovationserhebung 2013, Jg. 21, Nr. 10, Januar 2014.

[2] CIS surveys gather several data about activities within the German economy by caring out questionnaires for the last 20 years.

[3] Webpage: http://www.zew.de/en/publikationen/innovationserhebungen/innovationserhebungen.php3

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External search strategy and innovation performance. Effects on employee loyalty in the German automotive industry
Innovation Management
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ISBN (Book)
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Open Innovation, External Search Strategy, Innovation Performance, Employee Loyalty, Loyalty, Performance, Strategy, Search, Innovation, Strategien, Innovationen, R&D, Analyse
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Robert Högler (Author), 2015, External search strategy and innovation performance. Effects on employee loyalty in the German automotive industry, Munich, GRIN Verlag, https://www.grin.com/document/304500


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